code: def __init__(self, parameters, deterministic=False):
      self.parameters = parameters
      self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
      self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
      self.deterministic = deterministic
      self.std = torch.exp(0.5 * self.logvar)
      self.var = torch.exp(self.logvar)
      if self.deterministic:
          self.var = self.std = torch.zeros_like(self.mean).to(
              device=self.parameters.device
          )
  
file_path: ColossalAI/examples/images/diffusion/ldm/modules/distributions/distributions.py
imports: import numpy
  import torch
label_desc: Use image node_id 1 to create a new DiagonalGaussianDistribution object from inherited base classes: object with example: obj = DiagonalGaussianDistribution(parameters, deterministic)